Neman- a single parent- quit his job and started a small independent b.pdf
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Neman, a single parent, quit his job and started a small independent business because he was unable to give adequate attention to his child. Being an entrepreneur gives him the freedom to give undivided attention to his child without jeopardizing his work. Which of the following advantages of entrepreneurship is highlighted in the given scenario? Group of answer choices 1. Greater challenge 2. Survival 3. Flexibility 4. Greater financial success.
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#pragma once
#include <functional> // std::less
#include <iostream>
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#include <utility> // std::pair
template <typename K, typename V, typename Comparator = std::less<K>>
class BinarySearchTree
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public:
using key_type = K;
using value_type = V;
using key_compare = Comparator;
using pair = std::pair<key_type, value_type>;
using pointer = pair*;
using const_pointer = const pair*;
using reference = pair&;
using const_reference = const pair&;
using difference_type = ptrdiff_t;
using size_type = size_t;
private:
struct BinaryNode
{
pair element;
BinaryNode *left;
BinaryNode *right;
BinaryNode( const_reference theElement, BinaryNode *lt, BinaryNode *rt )
: element{ theElement }, left{ lt }, right{ rt } { }
BinaryNode( pair && theElement, BinaryNode *lt, BinaryNode *rt )
: element{ std::move( theElement ) }, left{ lt }, right{ rt } { }
};
using node = BinaryNode;
using node_ptr = node*;
using const_node_ptr = const node*;
node_ptr _root;
size_type _size;
key_compare comp;
public:
BinarySearchTree() {
// TODO
}
BinarySearchTree( const BinarySearchTree & rhs ) {
// TODO
}
BinarySearchTree( BinarySearchTree && rhs ) {
// TODO
}
~BinarySearchTree() {
// TODO
}
const_reference min() const { return min( _root )->element; }
const_reference max() const { return max( _root )->element; }
const_reference root() const {
// TODO
}
bool contains( const key_type & x ) const { return contains( x, _root ); }
value_type & find( const key_type & key ) { return find( key, _root )->element.second; }
const value_type & find( const key_type & key ) const { return find( key, _root )-
>element.second; }
bool empty() const {
// TODO
}
size_type size() const {
// TODO
}
void clear() {
clear( _root );
_size = 0;
}
void insert( const_reference x ) { insert( x, _root ); }
void insert( pair && x ) { insert( std::move( x ), _root ); }
void erase( const key_type & x ) { erase(x, _root); }
BinarySearchTree & operator=( const BinarySearchTree & rhs ) {
// TODO
}
BinarySearchTree & operator=( BinarySearchTree && rhs ) {
// TODO
}
private:
void insert( const_reference x, node_ptr & t ) {
// TODO
}
void insert( pair && x, node_ptr & t ) {
// TODO
}
void erase( const key_type & x, node_ptr & t ) {
// TODO
}
const_node_ptr min( const_node_ptr t ) const {
// TODO
}
const_node_ptr max( const_node_ptr t ) const {
// TODO
}
bool contains( const key_type & x, const_node_ptr t ) const {
// TODO
}
node_ptr find( const key_type & key, node_ptr t ) {
// TODO
}
const_node_ptr find( const key_type & key, const_node_ptr t ) const {
// TODO
}
void clear( node_ptr & t ) {
// TODO
}
node_ptr clone ( const_node_ptr t ) const {
// TODO
}
public:
template <typename KK, typename VV, typename CC>
friend void printLevelByLevel( const BinarySearchTree<KK, VV, CC>& bst, std::ostream & out
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template <typename KK, typename VV, typename CC>
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#Step 1: Import the required Python libraries:
import numpy as np
import matplotlib.pyplot as plt
import keras
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from keras.layers import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import Adam,SGD
from keras.datasets import cifar10
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#Loading the CIFAR10 data
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#Selecting a single class of images
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model.add(Reshape((8, 8, 128)))
model.add(UpSampling2D())
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model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(UpSampling2D())
model.add(Conv2D(64, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
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#Generating the output image
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#Step 5: Define a utility function to build the discriminator.
def build_discriminator():
#Building the convolutional layers
#to classify whether an image is real or fake
model = Sequential()
model.add(Conv2D(32, kernel_size=3, strides=2,
input_shape=image_shape, padding="same"))
model.add(LeakyReLU(alpha=0.2))
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model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
model.add(ZeroPadding2D(padding=((0,1),(0,1))))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(256, kernel_size=3, strides=1, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
#Building the output layer
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
image = Input(shape=image_shape)
validity = model(image)
return Model(image, validity)
#Step 6: Define a utility function to display the generated images.
def display_images():
r, c = 4,4
noise = np.random.normal(0, 1, (r * c,latent_dimensions))
generated_images = generator.predict(noise)
#Scaling the generated images
generated_images = 0.5 * genera.
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The code I have so far feels about 70% complete. (I plan to get rid of the qsort function and
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#include<stdio.h>
#include<stdlib.h>
#include<math.h>
#include<string.h>
#define MAX_N 500000
#define MAX_S 10
struct tugboat {
int x, y, dist;
char name[21];
};
int cmp(const void* a, const void* b) {
const struct tugboat* t1 = (const struct tugboat*) a;
const struct tugboat* t2 = (const struct tugboat*) b;
if (t1->dist != t2->dist) {
return t1->dist - t2->dist;
} else {
return strcmp(t1->name, t2->name);
}
}
int main() {
int xs, ys, xn, yn;
scanf("%d%d%d%d", &xs, &ys, &xn, &yn);
int n;
scanf("%d", &n);
struct tugboat tugboats[MAX_N];
int i;
for (i = 0; i < n; i++) {
scanf("%d%d%s", &tugboats[i].x, &tugboats[i].y, tugboats[i].name);
int dx = tugboats[i].x - xs;
int dy = tugboats[i].y - ys;
tugboats[i].dist = dx * dx + dy * dy;
}
qsort(tugboats, n, sizeof(struct tugboat), cmp);
int num_tugs = 0;
char tugs[MAX_N][21];
int prev_x = xs, prev_y = ys;
for (i = 0; i < n; i++) {
if (tugboats[i].x >= prev_x && tugboats[i].y >= prev_y) {
num_tugs++;
strcpy(tugs[num_tugs - 1], tugboats[i].name);
prev_x = tugboats[i].x;
prev_y = tugboats[i].y;
}
}
printf("%d\n", num_tugs);
for (i = 0; i < num_tugs; i++) {
printf("%s\n", tugs[i]);
}
return 0;
}
Here are the instructions:
Tugboat Troubles Objective Give practice with sorting in C. Story The ship's engine has ceased,
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shot to the harbor. The bad news is that the journey is long. There are several tugboats currently
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custodian is responsible. plus the that support payments from the fund. The internal control
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Need help with international accounting, please; thanks.
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object based on its id value (user supplies id and should display rental fee value to the user) -
selection to exit program 4) Program should throw and catch Java built-in and user-defined
exceptions as appropriate 5) Your classes must be coded with correct encapsulation:
private/protected attributes, get methods, and set methods and value validation 6) There should
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#pragma once
#include <functional> // std::less
#include <iostream>
#include <queue> // std::queue
#include <utility> // std::pair
template <typename K, typename V, typename Comparator = std::less<K>>
class BinarySearchTree
{
public:
using key_type = K;
using value_type = V;
using key_compare = Comparator;
using pair = std::pair<key_type, value_type>;
using pointer = pair*;
using const_pointer = const pair*;
using reference = pair&;
using const_reference = const pair&;
using difference_type = ptrdiff_t;
using size_type = size_t;
private:
struct BinaryNode
{
pair element;
BinaryNode *left;
BinaryNode *right;
BinaryNode( const_reference theElement, BinaryNode *lt, BinaryNode *rt )
: element{ theElement }, left{ lt }, right{ rt } { }
BinaryNode( pair && theElement, BinaryNode *lt, BinaryNode *rt )
: element{ std::move( theElement ) }, left{ lt }, right{ rt } { }
};
using node = BinaryNode;
using node_ptr = node*;
using const_node_ptr = const node*;
node_ptr _root;
size_type _size;
key_compare comp;
public:
BinarySearchTree() {
// TODO
}
BinarySearchTree( const BinarySearchTree & rhs ) {
// TODO
}
BinarySearchTree( BinarySearchTree && rhs ) {
// TODO
}
~BinarySearchTree() {
// TODO
}
const_reference min() const { return min( _root )->element; }
const_reference max() const { return max( _root )->element; }
const_reference root() const {
// TODO
}
bool contains( const key_type & x ) const { return contains( x, _root ); }
value_type & find( const key_type & key ) { return find( key, _root )->element.second; }
const value_type & find( const key_type & key ) const { return find( key, _root )-
>element.second; }
bool empty() const {
// TODO
}
size_type size() const {
// TODO
}
void clear() {
clear( _root );
_size = 0;
}
void insert( const_reference x ) { insert( x, _root ); }
void insert( pair && x ) { insert( std::move( x ), _root ); }
void erase( const key_type & x ) { erase(x, _root); }
BinarySearchTree & operator=( const BinarySearchTree & rhs ) {
// TODO
}
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// TODO
}
private:
void insert( const_reference x, node_ptr & t ) {
// TODO
}
void insert( pair && x, node_ptr & t ) {
// TODO
}
void erase( const key_type & x, node_ptr & t ) {
// TODO
}
const_node_ptr min( const_node_ptr t ) const {
// TODO
}
const_node_ptr max( const_node_ptr t ) const {
// TODO
}
bool contains( const key_type & x, const_node_ptr t ) const {
// TODO
}
node_ptr find( const key_type & key, node_ptr t ) {
// TODO
}
const_node_ptr find( const key_type & key, const_node_ptr t ) const {
// TODO
}
void clear( node_ptr & t ) {
// TODO
}
node_ptr clone ( const_node_ptr t ) const {
// TODO
}
public:
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template <typename KK, typename VV, typename CC>
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import numpy as np
import matplotlib.pyplot as plt
import keras
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from keras.layers import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import Adam,SGD
from keras.datasets import cifar10
#Step 2: Load the data.
#Loading the CIFAR10 data
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#Selecting a single class of images
#The number was randomly chosen and any number
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model.add(Dense(128 * 8 * 8, activation="relu",
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model.add(Conv2D(128, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(UpSampling2D())
model.add(Conv2D(64, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(Conv2D(3, kernel_size=3, padding="same"))
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model = Sequential()
model.add(Conv2D(32, kernel_size=3, strides=2,
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model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
model.add(ZeroPadding2D(padding=((0,1),(0,1))))
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model.add(Dropout(0.25))
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model.add(LeakyReLU(alpha=0.2))
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model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
#Building the output layer
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
image = Input(shape=image_shape)
validity = model(image)
return Model(image, validity)
#Step 6: Define a utility function to display the generated images.
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r, c = 4,4
noise = np.random.normal(0, 1, (r * c,latent_dimensions))
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#include<stdlib.h>
#include<math.h>
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#define MAX_N 500000
#define MAX_S 10
struct tugboat {
int x, y, dist;
char name[21];
};
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int main() {
int xs, ys, xn, yn;
scanf("%d%d%d%d", &xs, &ys, &xn, &yn);
int n;
scanf("%d", &n);
struct tugboat tugboats[MAX_N];
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int dy = tugboats[i].y - ys;
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qsort(tugboats, n, sizeof(struct tugboat), cmp);
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char tugs[MAX_N][21];
int prev_x = xs, prev_y = ys;
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num_tugs++;
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printf("%s\n", tugs[i]);
}
return 0;
}
Here are the instructions:
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This slide is prepared for master's students (MIFB & MIBS) UUM. May it be useful to all.678020731-Sumas-y-Restas-Para-Colorear.pdf
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Wednesday 22 May 2024, 14:00-15:00.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
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